mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
268 lines
11 KiB
Python
268 lines
11 KiB
Python
import os
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import json
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import time
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import asyncio
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import logging
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from typing import Dict, Set
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from ..config import config
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from ..services.service_registry import ServiceRegistry
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from ..metadata_collector.metadata_registry import MetadataRegistry
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from ..metadata_collector.constants import MODELS, LORAS
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logger = logging.getLogger(__name__)
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class UsageStats:
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"""Track usage statistics for models and save to JSON"""
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_instance = None
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_lock = asyncio.Lock() # For thread safety
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# Default stats file name
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STATS_FILENAME = "lora_manager_stats.json"
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def __new__(cls):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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if self._initialized:
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return
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# Initialize stats storage
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self.stats = {
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"checkpoints": {}, # sha256 -> count
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"loras": {}, # sha256 -> count
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"total_executions": 0,
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"last_save_time": 0
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}
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# Queue for prompt_ids to process
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self.pending_prompt_ids = set()
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# Load existing stats if available
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self._stats_file_path = self._get_stats_file_path()
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self._load_stats()
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# Save interval in seconds
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self.save_interval = 90 # 1.5 minutes
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# Start background task to process queued prompt_ids
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self._bg_task = asyncio.create_task(self._background_processor())
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self._initialized = True
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logger.info("Usage statistics tracker initialized")
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def _get_stats_file_path(self) -> str:
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"""Get the path to the stats JSON file"""
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if not config.loras_roots or len(config.loras_roots) == 0:
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# Fallback to temporary directory if no lora roots
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return os.path.join(config.temp_directory, self.STATS_FILENAME)
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# Use the first lora root
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return os.path.join(config.loras_roots[0], self.STATS_FILENAME)
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def _load_stats(self):
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"""Load existing statistics from file"""
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try:
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if os.path.exists(self._stats_file_path):
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with open(self._stats_file_path, 'r', encoding='utf-8') as f:
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loaded_stats = json.load(f)
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# Update our stats with loaded data
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if isinstance(loaded_stats, dict):
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# Update individual sections to maintain structure
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if "checkpoints" in loaded_stats and isinstance(loaded_stats["checkpoints"], dict):
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self.stats["checkpoints"] = loaded_stats["checkpoints"]
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if "loras" in loaded_stats and isinstance(loaded_stats["loras"], dict):
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self.stats["loras"] = loaded_stats["loras"]
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if "total_executions" in loaded_stats:
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self.stats["total_executions"] = loaded_stats["total_executions"]
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logger.info(f"Loaded usage statistics from {self._stats_file_path}")
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except Exception as e:
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logger.error(f"Error loading usage statistics: {e}")
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async def save_stats(self, force=False):
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"""Save statistics to file"""
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try:
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# Only save if it's been at least save_interval since last save or force is True
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current_time = time.time()
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if not force and (current_time - self.stats.get("last_save_time", 0)) < self.save_interval:
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return False
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# Use a lock to prevent concurrent writes
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async with self._lock:
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# Update last save time
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self.stats["last_save_time"] = current_time
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# Create directory if it doesn't exist
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os.makedirs(os.path.dirname(self._stats_file_path), exist_ok=True)
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# Write to a temporary file first, then move it to avoid corruption
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temp_path = f"{self._stats_file_path}.tmp"
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with open(temp_path, 'w', encoding='utf-8') as f:
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json.dump(self.stats, f, indent=2, ensure_ascii=False)
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# Replace the old file with the new one
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os.replace(temp_path, self._stats_file_path)
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logger.debug(f"Saved usage statistics to {self._stats_file_path}")
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return True
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except Exception as e:
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logger.error(f"Error saving usage statistics: {e}", exc_info=True)
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return False
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def register_execution(self, prompt_id):
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"""Register a completed execution by prompt_id for later processing"""
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if prompt_id:
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self.pending_prompt_ids.add(prompt_id)
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async def _background_processor(self):
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"""Background task to process queued prompt_ids"""
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try:
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while True:
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# Wait a short interval before checking for new prompt_ids
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await asyncio.sleep(5) # Check every 5 seconds
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# Process any pending prompt_ids
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if self.pending_prompt_ids:
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async with self._lock:
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# Get a copy of the set and clear original
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prompt_ids = self.pending_prompt_ids.copy()
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self.pending_prompt_ids.clear()
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# Process each prompt_id
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registry = MetadataRegistry()
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for prompt_id in prompt_ids:
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try:
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metadata = registry.get_metadata(prompt_id)
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await self._process_metadata(metadata)
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except Exception as e:
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logger.error(f"Error processing prompt_id {prompt_id}: {e}")
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# Periodically save stats
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await self.save_stats()
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except asyncio.CancelledError:
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# Task was cancelled, clean up
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await self.save_stats(force=True)
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except Exception as e:
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logger.error(f"Error in background processing task: {e}", exc_info=True)
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# Restart the task after a delay if it fails
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asyncio.create_task(self._restart_background_task())
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async def _restart_background_task(self):
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"""Restart the background task after a delay"""
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await asyncio.sleep(30) # Wait 30 seconds before restarting
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self._bg_task = asyncio.create_task(self._background_processor())
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async def _process_metadata(self, metadata):
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"""Process metadata from an execution"""
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if not metadata or not isinstance(metadata, dict):
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return
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# Increment total executions count
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self.stats["total_executions"] += 1
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# Process checkpoints
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if MODELS in metadata and isinstance(metadata[MODELS], dict):
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await self._process_checkpoints(metadata[MODELS])
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# Process loras
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if LORAS in metadata and isinstance(metadata[LORAS], dict):
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await self._process_loras(metadata[LORAS])
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async def _process_checkpoints(self, models_data):
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"""Process checkpoint models from metadata"""
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try:
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# Get checkpoint scanner service
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checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
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if not checkpoint_scanner:
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logger.warning("Checkpoint scanner not available for usage tracking")
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return
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for node_id, model_info in models_data.items():
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if not isinstance(model_info, dict):
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continue
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# Check if this is a checkpoint model
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model_type = model_info.get("type")
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if model_type == "checkpoint":
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model_name = model_info.get("name")
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if not model_name:
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continue
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# Clean up filename (remove extension if present)
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model_filename = os.path.splitext(os.path.basename(model_name))[0]
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# Get hash for this checkpoint
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model_hash = checkpoint_scanner.get_hash_by_filename(model_filename)
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if model_hash:
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# Update stats for this checkpoint
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self.stats["checkpoints"][model_hash] = self.stats["checkpoints"].get(model_hash, 0) + 1
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except Exception as e:
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logger.error(f"Error processing checkpoint usage: {e}", exc_info=True)
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async def _process_loras(self, loras_data):
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"""Process LoRA models from metadata"""
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try:
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# Get LoRA scanner service
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lora_scanner = await ServiceRegistry.get_lora_scanner()
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if not lora_scanner:
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logger.warning("LoRA scanner not available for usage tracking")
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return
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for node_id, lora_info in loras_data.items():
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if not isinstance(lora_info, dict):
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continue
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# Get the list of LoRAs from standardized format
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lora_list = lora_info.get("lora_list", [])
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for lora in lora_list:
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if not isinstance(lora, dict):
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continue
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lora_name = lora.get("name")
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if not lora_name:
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continue
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# Get hash for this LoRA
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lora_hash = lora_scanner.get_hash_by_filename(lora_name)
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if lora_hash:
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# Update stats for this LoRA
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self.stats["loras"][lora_hash] = self.stats["loras"].get(lora_hash, 0) + 1
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except Exception as e:
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logger.error(f"Error processing LoRA usage: {e}", exc_info=True)
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async def get_stats(self):
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"""Get current usage statistics"""
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return self.stats
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async def get_model_usage_count(self, model_type, sha256):
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"""Get usage count for a specific model by hash"""
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if model_type == "checkpoint":
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return self.stats["checkpoints"].get(sha256, 0)
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elif model_type == "lora":
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return self.stats["loras"].get(sha256, 0)
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return 0
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async def process_execution(self, prompt_id):
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"""Process a prompt execution immediately (synchronous approach)"""
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if not prompt_id:
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return
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try:
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# Process metadata for this prompt_id
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registry = MetadataRegistry()
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metadata = registry.get_metadata(prompt_id)
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if metadata:
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await self._process_metadata(metadata)
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# Save stats if needed
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await self.save_stats()
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except Exception as e:
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logger.error(f"Error processing prompt_id {prompt_id}: {e}", exc_info=True)
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